Databricks Data Intelligence Platform vs. Qrvey

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

Incentivized

For our use case, its hands down the ability to embed visualizations onto our platform. We have an intelligence platform that provides different types of insights that follow a specific user flow. We need a platform that could not only visualize our data but help automate our data processing workflows. Other analytics platforms that we have used in the past make it extremely difficult to do the same thing.

• There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
• There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.

Incentivized

• The platform deploys to our AWS instance which gave us the ability to manage ourselves
• The feature of easily embedding visualizations into other applications
• The powerful features like workflow automation

• Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
• Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
• Visualization in MLFLOW experiment can be enhanced

Incentivized

• Merges between heterogenous datasets can occasionally be problematic
• The dashboards/pages are already very powerful, but more interactive elements would always be welcome.
• The administration of a Qrvey instance and underlying datastore can sometimes be complex, but the Admin app helps make this a smooth process.

Incentivized

Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

Incentivized

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

Databricks is a true all-in-one platform, and at the time of implementation, it had more features available to us, making it a clear choice over Snowflake. Moving our workloads from local computing to the servers in Databricks gave our start-up staff a great quality of life boost.

Incentivized

Qrvey isn't just an analytics tool or a survey tool. It's a one-stop-shop for surveys, analytics, and automation. If you need all or most of this functionality, Qrvey can be a better solution than standing up three tools and getting them to work well together. Looking at just analytics, Qrvey is excellent for embedded analytics on AWS. If you need to put a chart or a chart/dashboard builder in your website or web app, Qrvey makes this process very straightforward.

Incentivized

• ROI for us has been tremendous. Time to market by processing raw data in our big data infrastructure has been pretty fast.
• Non engineers can easily use Databricks, hence helping business customers.
• Thousands of different data combinations can easily be joined and used by our data teams.

Incentivized

• In one implementation, Qrvey helped our customer quickly achieve feature parity with competitors, reducing churn.

Incentivized